Deep Learning Algorithms and Parallel Distributed Computing Techniques for High-Resolution Load Forecasting Applying Hyperparameter Optimization

Electrical load forecasting is one of the critical tasks that helps power utility companies in planning and operation as well as the energy managementsystem (EMS) in controlling and optimizing the power grid's performance. It guarantees system adequacy and reliability by reducing the gap betwee...

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Veröffentlicht in:	IEEE systems journal Jg. 16; H. 3; S. 3758 - 3769
Hauptverfasser:	Parizad, Ali, Hatziadoniu, Constantine
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adequacy Algorithms Artificial neural networks Big data analytics Computational modeling Computer networks Cybersecurity Deep learning deep learning (DL) algorithms Distributed processing Electric power grids Electric utilities Electrical loads Forecasting gated recurrent unit (GRU) hyperparameter optimization Load forecasting Load modeling Logic gates long short term memory (LSTM) Machine learning Meteorology Optimization parallel distributed computing (PDC) Peak load Predictive models Searching sequence prediction Weather forecasting
ISSN:	1932-8184, 1937-9234
Online-Zugang:	Volltext
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